An efficient global optimization method for self-potential data inversion using micro-differential evolution

Sungkono, Sungkono

doi:10.1007/s12040-020-01430-z

Cited by 19 publications

(5 citation statements)

References 55 publications

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“…In the applications, we also used the SPSO algorithm. SPSO and some of its variants have been commonly used in inverse problems for geophysics (Ekinci, 2016;Ekinci, Balkaya, & Göktürkler, 2020;Essa & Elhussein, 2018, 2020Essa et al, 2021;Fernández-Martínez et al, 2010;Liu et al, 2018;Pace et al, 2021;Pallero et al, 2015;Pekşen et al, 2014;Santos, 2010;Singh & Biswas, 2016;Song et al, 2012). Using the optimum control parameters of HGS, inversion procedures were performed and the solutions obtained were compared with the outputs of SPSO algorithm.…”

Section: Synthetic Anomaly Casesmentioning

confidence: 99%

“…However, because of the well‐known ill‐posedness and non‐uniqueness nature of the geomagnetic data inversion problem, explanation of anomaly sources, that is, model parameter estimations, necessitate some special strategies and efficient approaches (Ekinci et al., 2019). Over the recent years, instead of derivative‐based local optimizers, derivative‐free nature‐inspired global optimizers and metaheuristics such as Particle Swarm Optimization (PSO) (Essa, Abo‐Ezz, et al., 2022; Essa & Elhussein, 2020; Fernández‐Martínez et al., 2010; Pallero et al., 2015; Roy et al., 2022; Santos, 2010), Very Fast Simulated Annealing (VFSA) (Biswas, 2016; Biswas & Acharya, 2016; Biswas & Rao, 2021), Ant Colony Optimization (Liu et al., 2014, 2015; Srivastava et al., 2014); Gray Wolf Optimizer (Agarwal et al., 2018; Chandra et al., 2017), Genetic‐Price Algorithm (Di Maio et al., 2020), Cuckoo Search Algorithm (Turan‐Karaoğlan & Göktürkler, 2021), Differential Search Algorithm (Alkan & Balkaya, 2018; A. Balkaya & Kaftan, 2021; Özyalın & Sındırgı, 2023), Bat Algorithm (Essa & Diab, 2022; Gobashy et al., 2021), Differential Evolution Algorithm (Ç. Balkaya, 2013; Du et al., 2021; Ekinci, Balkaya, & Göktürkler, 2020; Ekinci et al., 2023; Göktürkler et al., 2016; Hosseinzadeh et al., 2023; Roy et al., 2021a; Sungkono, 2020); Backtracking Search Algorithm (Ekinci, Balkaya, & Göktürkler, 2021), Manta‐Ray Foraging Optimization and Social Spider Optimization (Ben et al., 2022a, 2022b, 2022c), Barnacles Mating Optimization (BMO) (Ai et al., 2022) have gained increasing attention in geophysical inversion applications. Unlike local search algorithms, these stochastic optimizers do not need a well‐designed starting point in the model space to reach the global minimum (Sen & Stoffa, 2013; Tarantola, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Balkaya & Kaftan, 2021;Özyalın & Sındırgı, 2023), Bat Algorithm (Essa & Diab, 2022;Gobashy et al, 2021), Differential Evolution Algorithm (Ç. Balkaya, 2013;Du et al, 2021;Ekinci, Balkaya, & Göktürkler, 2020;Ekinci et al, 2023;Göktürkler et al, 2016;Hosseinzadeh et al, 2023;Roy et al, 2021a;Sungkono, 2020); Backtracking Search Algorithm , Manta-Ray Foraging Optimization and Social Spider Optimization (Ben et al, 2022a(Ben et al, , 2022b(Ben et al, , 2022c, Barnacles Mating Optimization (BMO) (Ai et al, 2022) have gained increasing attention in geophysical inversion applications. Unlike local search algorithms, these stochastic optimizers do not need a well-designed starting point in the model space to reach the global minimum (Sen & Stoffa, 2013;Tarantola, 2005).…”

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confidence: 99%

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Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

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Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

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Section: Synthetic Anomaly Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

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“…However, due to the presence of noise and the nonlinear relationship between the model parameters and the data, these techniques, which primarily aim to find the best solution in a small region of the solution space around the starting point, require prior information and some constraints to determine an appropriate model estimate (Meju, 1994;Menke, 1989;Tarantola, 2005;Zhdanov, 2002). Therefore, global optimization using metaheuristics has gained prominence in geophysics over gradient-based local inversion in the last decade (Balkaya, 2013;Balkaya et al, 2017;Biswas et al, 2017;Chandra et al, 2017;Ekinci et al, 2016Essa & Elhussein, 2018, 2020Göktürkler et al, 2016;Pace et al, 2019;Pallero et al, 2021;Roy et al, 2022;Santilano et al, 2018;Singh & Biswas, 2016;Sungkono, 2020). These algorithms are designed to search the entire solution space and can handle unconstrained optimization problems (Robert & Casella, 2005;Sen & Stoffa, 2013).…”

Section: Introductionmentioning

confidence: 99%

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

Balkaya,

Ekinci,

et al. 2024

Earth and Space Science

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Fault structure reconstruction is an important aspect of magnetic prospecting as it contributes to understanding tectonic history, hazard assessment, and infrastructure planning. Metaheuristics have proven useful in estimating fault structure parameters from geophysical anomalies. Recently, a modified version of the Backtracking Search Optimization Algorithm (BSA), named mBSA, has been proposed that combines mutation steps of BSA and Differential Evolution (DE) algorithms to achieve a better balance between exploration and exploitation. Here, we present imBSA as an efficient approach that builds on the improvements of mBSA. It uses an adaptive amplitude control factor in the mutation step derived from a normally distributed random number obtained via an exponential function. The performances of these approaches were tested on some widely used benchmark functions such as Rosenbrock, Rastrigin, Griewank, and Himmelblau. Synthetic magnetic fault anomalies were evaluated with and without noise, as well as three real anomalies from the Dehri (India), Perth Basin, and Lachlan Fold Belt (Australia). Prior to the optimizations, the solvability of the model parameters was examined using some error topography images. The obtained solutions in the synthetic cases were evaluated using principal component analysis. In addition, a novel visualization option for high‐dimensional optimization problems was introduced. The experiments showed that imBSA has a faster convergence rate and better optimization statistics compared to BSA and mBSA. Therefore, it is believed to be an efficient and good alternative tool to widely used metaheuristics such as Particle Swarm Optimization and DE in explaining geophysical anomalies and optimization problems in other geoscientific disciplines.

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“…Due to its limitations in delineating an effective solution, global optimization or metaheuristic optimization is necessary for finding an optimal solution that does not need an initial guess of the model parameters and can give the best result. Global optimization such as the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE) algorithm, very fast simulated annealing (VFSA), genetic-price algorithm (GPO), and whale optimization algorithm (WOA) has been applied to numerous geophysical applications such as seismic data [56][57][58], self-potential data [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73], and also in the interpretation of gravity and magnetic data [3,[74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92].…”

Section: Introductionmentioning

confidence: 99%

Interpretation of Magnetic Anomalies over 2D Fault and Sheet-Type Mineralized Structures Using Very Fast Simulated Annealing Global Optimization: An Understanding of Uncertainty and Geological Implications

Biswas

Rao

2021

Lithosphere

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Identification of intraterrane dislocation zones and associated mineralized bodies is of immense importance in exploration geophysics. Understanding such structures from geophysical anomalies is challenging and cumbersome. In the present study, we present a fast and competent algorithm for interpreting magnetic anomalies from such dislocation and mineralized zones. Such dislocation and mineralized zones are well explained from 2D fault and sheet-type structures. The different parameters from 2D fault and sheet-type structures such as the intensity of magnetization (k), depth to the top (z1), depth to the bottom (z2), origin location (x0), and dip angle (θ) of the fault and sheet from magnetic anomalies are interpreted. The interpretation suggests that there is uncertainty in defining the model parameters z1 and z2 for the 2D inclined fault; k, z1, and z2 for the 2D vertical fault and finite sheet-type structure; and k and z for the infinite sheet-type structure. Here, it shows a wide range of solutions depicting an equivalent model with smaller misfits. However, the final interpreted mean model is close to the actual model with the least uncertainty. Histograms and crossplots for 2D fault and sheet-type structures also reveal the same. The present algorithm is demonstrated with four theoretical models, including the effect of noises. Furthermore, the investigation of magnetic data was also applied from three field examples from intraterrane dislocation zones (Australia), deep-seated dislocation zones (India) as a 2D fault plane, and mineralized zones (Canada) as sheet-type structures. The final estimated model parameters are in good agreement with the earlier methods applied for these field examples with a priori information wherever available in the literature. However, the present method can simultaneously interpret all model parameters without a priori information.

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An efficient global optimization method for self-potential data inversion using micro-differential evolution

Cited by 19 publications

References 55 publications

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

Interpretation of Magnetic Anomalies over 2D Fault and Sheet-Type Mineralized Structures Using Very Fast Simulated Annealing Global Optimization: An Understanding of Uncertainty and Geological Implications

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